In order to achieve a robust video delivery over Internet and error-prone wireless networks under the constraint of limited bandwidth, Quality of Service (QoS) provisioning techniques have been proposed, ranging from MAC layer (e.g. ARQ and hybrid ARQ in WiMAX network) to application layer (e.g., error-resilient video coding in H.264). Normally, these QoS provisioning techniques will be helpful to improve the experience of an end user on a video service, so long as they can guarantee a certain QoS performance level which can be measured by QoS metrics, such as packet loss rate, delay, available bandwidth, etc.
However, QoS is only a metric of network performance but not equal to the Quality of Experience (QoE) of the video service observed by an end user, which is considered to be the ultimate goal from a service provider's point of view. Video QoE depends on not only network performance but also specific video applications and a viewer's vision system. For example, at a packet loss rate of 1%, the perceived video quality is still good for some video contents with lower motion activities, while the visible artefact in this case will be annoying for some other video contents with higher motion activities. Therefore, QoS adjustment is not always effective for QoE management of the video service.
In addition, once a video system is designed for a target QoS for certain applications, the system performance and adopted QoS provisioning techniques are fixed. User can do nothing to control the quality of the received video service even if the perceived quality of interested video program is not satisfactory. The target QoS level is generally designed according to the worst channel condition in order to guarantee consistent good video QoE, which can be a waste of network resources. Although the technical problem of the conventional art is explained with regard to the video service, a person skilled in the art can appreciate that other network services have the same disadvantage.
Finally, in a home network, there may be several running applications, e.g. file downloading, TV programs streaming and web browsing, etc. When these applications compete for resources and adopt independent techniques to improve their respective QoS, an even worse network deterioration might be generated, which in turn will lead to an even worse QoE for end users. Again, users can do nothing about the QoE degradation.